The Stackelberg minimum spanning tree game

Jean Cardinal, Erik D. Demaine, Samuel Fiorini, Gwenaël Joret, Stefan Langerman, Ilan Newman, Oren Weimann

Research output: Contribution to journalArticlepeer-review


We consider a one-round two-player network pricing game, the Stackelberg Minimum Spanning Tree game or StackMST. The game is played on a graph (representing a network), whose edges are colored either red or blue, and where the red edges have a given fixed cost (representing the competitor's prices). The first player chooses an assignment of prices to the blue edges, and the second player then buys the cheapest possible minimum spanning tree, using any combination of red and blue edges. The goal of the first player is to maximize the total price of purchased blue edges. This game is the minimum spanning tree analog of the well-studied Stackelberg shortest-path game. We analyze the complexity and approximability of the first player's best strategy in StackMST. In particular, we prove that the problem is APX-hard even if there are only two different red costs, and give an approximation algorithm whose approximation ratio is at most min {k,1+ln b,1+ln W}, where k is the number of distinct red costs, b is the number of blue edges, and W is the maximum ratio between red costs. We also give a natural integer linear programming formulation of the problem, and show that the integrality gap of the fractional relaxation asymptotically matches the approximation guarantee of our algorithm.

Original languageEnglish
Pages (from-to)129-144
Number of pages16
Issue number2
StatePublished - Feb 2011

Bibliographical note

Funding Information:
A preliminary version of this article appeared in the Proceedings of the 10th Workshop on Algorithms and Data Structures (WADS 2007), see []. This work was partially supported by the Actions de Recherche Concertées (ARC) fund of the Communauté française de Belgique.


  • Approximation algorithms
  • Combinatorial optimization
  • Maximum-revenue pricing
  • Spanning trees

ASJC Scopus subject areas

  • General Computer Science
  • Computer Science Applications
  • Applied Mathematics


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